Join Shane Emmons of Swept AI as he does a dive deep into AI model drift, where unexpected behavior is guaranteed in the UMBC Training Centers Center for Appllied AI lunchbox series.
Drift in machine learning models is a change in the data generating process that can cause a model's predictions to become less accurate or irrelevant over time. Inspired by the ranger service’s prevention and preparedness mentality, Shane will discuss the challenges of maintaining robust AI systems. Attend this session to learn to scrutinize models effectively, preventing them from spiraling out of control and enhancing their long-term success.
Get more information and register here for the link.
UMBC Center for AI